SoroushVahidi/scidocs huggingface dataset:
Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape
(1987)

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's “tactile sensor.” This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.
Obsessive-compulsive disorder and gut microbiota dysregulation.
(2014)

Obsessive-compulsive disorder (OCD) is a debilitating disorder for which the cause is not known and treatment options are modestly beneficial. A hypothesis is presented wherein the root cause of OCD is proposed to be a dysfunction of the gut microbiome constituency resulting in a susceptibility to obsessional thinking. Both stress and antibiotics are proposed as mechanisms by which gut microbiota are altered preceding the onset of OCD symptomology. In this light, pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS) leading to episodic OCD is explained not by group A beta-hemolytic streptococcal infections, but rather by prophylactic antibiotics that are administered as treatment. Further, stressful life events known to trigger OCD, such as pregnancy, are recast to show the possibility of altering gut microbiota prior to onset of OCD symptoms. Suggested treatment for OCD would be the directed, specie-specific (re)introduction of beneficial bacteria modifying the gut microbiome, thereby ameliorating OCD symptoms. Special considerations should be contemplated when considering efficacy of treatment, particularly the unhealthy coping strategies often observed in patients with chronic OCD that may need addressing in conjunction with microbiome remediation.
Event-Based Mobile Social Networks: Services, Technologies, and Applications
(2014)

Event-based mobile social networks (MSNs) are a special type of MSN that has an immanently temporal common feature, which allows any smart phone user to create events to share group messaging, locations, photos, and insights among participants. The emergence of Internet of Things and event-based social applications integrated with context-awareness ability can be helpful in planning and organizing social events like meetings, conferences, and tradeshows. This paper first provides review of the event-based social networks and the basic principles and architecture of event-based MSNs. Next, event-based MSNs with smartphone contained technology elements, such as context-aware mobility and multimedia sharing, are presented. By combining the feature of context-aware mobility with multimedia sharing in event-based MSNs, event organizers, and planners with the service providers optimize their capability to recognize value for the multimedia services they deliver. The unique features of the current event-based MSNs give rise to the major technology trends to watch for designing applications. These mobile applications and their main features are described. At the end, discussions on the evaluation of the event-based mobile applications based on their main features are presented. Some open research issues and challenges in this important area of research are also outlined.
Fully Integrated Startup at 70 mV of Boost Converters for Thermoelectric Energy Harvesting
(2016)

This paper presents an inductive DC-DC boost converter for energy harvesting using a thermoelectric generator with a minimum startup voltage of 70 mV and a regulated output voltage of 1.25 V. With a typical generator resistance of 40 Ω, an output power of 17 μW can be provided, which translates to an end-to-end efficiency of 58%. The converter employs Schmitt-trigger logic startup control circuitry and an ultra-low voltage charge pump using modified Schmitt-trigger driving circuits optimized for driving capacitive loads. Together with a novel ultra-low leakage power switch and the required control scheme, to the best of the authors' knowledge, this enables the lowest minimum voltage with fully integrated startup.
Comparison of perioperative outcomes between robotic and laparoscopic partial nephrectomy: a systematic review and meta-analysis.
(2015)

CONTEXT
Robotic partial nephrectomy (RPN) is rapidly increasing; however, the benefit of RPN over laparoscopic partial nephrectomy (LPN) is controversial.


OBJECTIVE
To compare perioperative outcomes of RPN and LPN.


EVIDENCE ACQUISITION
We searched Ovid-Medline, Ovid-Embase, the Cochrane Library, KoreaMed, KMbase, KISS, RISS, and KisTi from their inception through August 2013. Two independent reviewers extracted data using a standardized form. Quality of the selected studies was assessed using the methodological index for nonrandomized studies.


EVIDENCE SYNTHESIS
A total of 23 studies and 2240 patients were included. All studies were cohort studies with no randomization, and the methodological quality varied. There was no significant difference between the two groups regarding complications of Clavien-Dindo classification grades 1-2 (p=0.62), Clavien-Dindo classification grades 3-5 (p=0.78), change of serum creatinine (p=0.65), operative time (p=0.35), estimated blood loss (p=0.76), and positive margins (p=0.75). The RPN group had a significantly lower rate of conversion to open surgery (p=0.02) and conversion to radical surgery (p=0.0006), shorter warm ischemia time (WIT; p=0.005), smaller change of estimated glomerular filtration rate (eGFR; p=0.03), and shorter length of stay (LOS; p=0.004).


CONCLUSIONS
This meta-analysis shows that RPN is associated with more favorable results than LPN in conversion rate to open or radical surgery, WIT, change of eGFR, and shorter LOS. To establish the safety and effectiveness outcomes of robotic surgery, well-designed randomized clinical studies with long-term follow-up are needed.


PATIENT SUMMARY
Robotic partial nephrectomy (PN) is more favorable than laparoscopic PN in terms of lower conversion rate to radical nephrectomy, a favorable renal function indexed estimated glomerular filtration rate, shorter length of hospital stay, and shorter warm ischemia time.
Bonding-wire triangular spiral inductor for on-chip switching power converters
(2011)

This work presents the first design and modelling of bonding-wire-based triangular spiral inductors (Fig. 1), targeting their application to on-chip switching power converters. It is demonstrated that the equilateral triangular shape compared to other polygonal shapes best balances the inductive density as well as the total Equivalent Series Resistance (ESR). Afterwards, a design procedure is presented in order to optimize the inductor design, in terms of ESR and occupied area reduction. Finally, finite-elements simulation results of an optimized design (27nH, 1 Ω) are presented to validate the proposed expressions.
Formal Analysis of Enhanced Authorization in the TPM 2.0
(2015)

The Trusted Platform Module (TPM) is a system component that provides a hardware-based approach to establish trust in a platform by providing protected storage, robust platform integrity measurement, secure platform attestation and other secure functionalities. The access to TPM commands and TPM-resident key objects are protected via an authorization mechanism. Enhanced Authorization (EA) is a new mechanism introduced by the TPM 2.0 to provide a rich authorization model for specifying flexible access control policies for TPM-resident objects.
 In our paper, we conduct a formal verification of the EA mechanism. Firstly, we propose a model of the TPM 2.0 EA mechanism in a variant of the applied pi calculus. Secondly, we identify and formalize the security properties of the EA mechanism (Prop.1 and 2) in its design. We also give out a misuse problem that is easily to be neglected (Lemma 7). Thirdly, using the SAPIC tool and the tamarin prover, we have verified both the two security properties. Meanwhile, we have found 3 misuse cases and one of them leads to an attack on the application in [12].
Application of Data Mining Techniques in IoT: A Short Review
(2018)

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Interfacing an every object together through internet looks very difficult, but within a frame of time Internet of Things will drastically change our life. The enormous data captured by the Internet of Things (IoT) are considered of high business as well as social values and extracting hidden information from raw data, various data mining algorithm can be applied to IoT data. In this paper, We survey systematic review of various data mining models as well as its application in Internet of Thing (IoT) field along with its merits and demerits. At last, we discussed challenges in IoT.
Large scale metric learning from equivalence constraints
(2012)

In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective. In contrast to existing methods we do not rely on complex optimization problems requiring computationally expensive iterations. Hence, our method is orders of magnitudes faster than comparable methods. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include faces in unconstrained environments, matching before unseen object instances and person re-identification across spatially disjoint cameras. In the latter two benchmarks we clearly outperform the state-of-the-art.
Universal approximation using incremental constructive feedforward networks with random hidden nodes
(2006)

According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g : R --> R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g : R --> R and integral of R g(x)dx not equal to 0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters.
Robust Discriminative Response Map Fitting with Constrained Local Models
(2013)

We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms state-of-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code and the pre-trained models for research purposes.
Permission Re-Delegation: Attacks and Defenses
(2011)

Modern browsers and smartphone operating systems treat applications as mutually untrusting, potentially malicious principals. Applications are (1) isolated except for explicit IPC or inter-application communication channels and (2) unprivileged by default, requiring user permission for additional privileges. Although inter-application communication supports useful collaboration, it also introduces the risk of permission redelegation. Permission re-delegation occurs when an application with permissions performs a privileged task for an application without permissions. This undermines the requirement that the user approve each application’s access to privileged devices and data. We discuss permission re-delegation and demonstrate its risk by launching real-world attacks on Android system applications; several of the vulnerabilities have been confirmed as bugs. We discuss possible ways to address permission redelegation and present IPC Inspection, a new OS mechanism for defending against permission re-delegation. IPC Inspection prevents opportunities for permission redelegation by reducing an application’s permissions after it receives communication from a less privileged application. We have implemented IPC Inspection for a browser and Android, and we show that it prevents the attacks we found in the Android system applications.
Spatio-temporal proximity and social distance: a confirmation framework for social reporting
(2010)

Social reporting is based on the idea that the members of a location-based social network observe real-world events and publish reports about their observations. Application scenarios include crisis management, bird watching or even some sorts of mobile games. A major issue in social reporting is the quality of the reports. We propose an approach to the quality problem that is based on the reciprocal confirmation of reports by other reports. This contrasts with approaches that require users to verify reports, that is, to explicitly evaluate their veridicality. We propose to use spatio-termporal proximity as a first criterion for confirmation and social distance as a second one. By combining these two measures we construct a graph containing the reports as nodes connected by confirmation edges that can adopt positive as well as negative values. This graph builds the basis for the computation of confirmation values for individual reports by different aggregation measures. By applying our approach to two use cases, we show the importance of a weighted combination, since the meaningfulness of the constituent measures varies between different contexts.
Modeling and Verification of a Six-Phase Interior Permanent Magnet Synchronous Motor
(2018)

In this paper, a new mathematical modeling for a six-phase interior permanent magnet synchronous motor (IPMSM) is presented. The proposed model utilizes two synchronous reference frames. First, the flux model in the <inline-formula> <tex-math notation="LaTeX">$abcxyz$</tex-math></inline-formula> frame is mapped into the stationary <inline-formula> <tex-math notation="LaTeX">$dq$</tex-math></inline-formula> frames and then to two synchronous rotating frames. Then, differentiating the flux models, voltage equations are derived in rotating frames. Through this analysis, the interaction between the <inline-formula><tex-math notation="LaTeX">$abc$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$xyz$</tex-math></inline-formula> subsystems is properly described by a coupling matrix. The torque equation is also derived using the two reference current variables. Flux model was verified through FEM analysis. Experiments were done using a 100 kW six-phase IPMSM in a dynamo system. The validity of the torque equation was checked with some experimental results under a shorted condition on an <inline-formula><tex-math notation="LaTeX">$xyz$</tex-math></inline-formula> subsystem.
Autonomous exploration of motor skills by skill babbling
(2017)

Autonomous exploration of motor skills is a key capability of learning robotic systems. Learning motor skills can be formulated as inverse modeling problem, which targets at finding an inverse model that maps desired outcomes in some task space, e.g., via points of a motion, to appropriate actions, e.g., motion control policy parameters. In this paper, autonomous exploration of motor skills is achieved by incrementally learning inverse models starting from an initial demonstration. The algorithm is referred to as skill babbling, features sample-efficient learning, and scales to high-dimensional action spaces. Skill babbling extends ideas of goal-directed exploration, which organizes exploration in the space of goals. The proposed approach provides a modular framework for autonomous skill exploration by separating the learning of the inverse model from the exploration mechanism and a model of achievable targets, i.e. the workspace. The effectiveness of skill babbling is demonstrated for a range of motor tasks comprising the autonomous bootstrapping of inverse kinematics and parameterized motion primitives.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
(2018)

Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep network for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements, i.e., anytime property. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.
Marketing actions can modulate neural representations of experienced pleasantness.
(2008)

Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.
A glasses-type wearable device for monitoring the patterns of food intake and facial activity
(2017)

Here we present a new method for automatic and objective monitoring of ingestive behaviors in comparison with other facial activities through load cells embedded in a pair of glasses, named GlasSense. Typically, activated by subtle contraction and relaxation of a temporalis muscle, there is a cyclic movement of the temporomandibular joint during mastication. However, such muscular signals are, in general, too weak to sense without amplification or an electromyographic analysis. To detect these oscillatory facial signals without any use of obtrusive device, we incorporated a load cell into each hinge which was used as a lever mechanism on both sides of the glasses. Thus, the signal measured at the load cells can detect the force amplified mechanically by the hinge. We demonstrated a proof-of-concept validation of the amplification by differentiating the force signals between the hinge and the temple. A pattern recognition was applied to extract statistical features and classify featured behavioral patterns, such as natural head movement, chewing, talking, and wink. The overall results showed that the average F1 score of the classification was about 94.0% and the accuracy above 89%. We believe this approach will be helpful for designing a non-intrusive and un-obtrusive eyewear-based ingestive behavior monitoring system.
A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids
(2016)

Mining grid data is an interesting research field which aims at analyzing grid systems with data mining techniques in order to efficiently discover new meaningful knowledge to enhance grid management. In this paper, we focus particularly on how extracted knowledge enables enhancing data replication and replica selection strategies which are important data management techniques commonly used in data grids. Indeed, relevant knowledge such as file access patterns, file correlations, user or job access behavior, prediction of future behavior or network performance, and so on, can be efficiently discovered. These findings are then used to enhance both data replication and replica selection strategies. Various works in this respect are then discussed along with their merits and demerits. In addition, we propose a new guideline to data mining application in the context of data replication and replica selection strategies. & 2015 Elsevier Ltd. All rights reserved.